Skip to content
← Back to explorer

HFEPX Benchmark Hub

MMLU Or GPQA Or WebArena Benchmark Papers

Updated from current HFEPX corpus (Apr 17, 2026). 39 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 39 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: MMLU. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Mar 22, 2026.

Papers: 39 Last published: Mar 22, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Medium .

High-Signal Coverage

100.0%

39 / 39 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 15 papers explicitly name benchmark datasets in the sampled set.
  • 9 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 46.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 17.9% of papers in this hub.
  • MMLU is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • MMLU appears in 46.7% of hub papers (7/39); use this cohort for benchmark-matched comparisons.
  • WebArena appears in 33.3% of hub papers (5/39); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 46.7% of hub papers (7/39); compare with a secondary metric before ranking methods.
  • cost is reported in 26.7% of hub papers (4/39); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

 Human Eval, Llm As Judge Demonstrations Precision, Pass@1 Not reported
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

 Llm As Judge, Automatic Metrics Expert Verification Accuracy, Relevance Not reported
How Reliable is Language Model Micro-Benchmarking?

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost Not reported
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

 Simulation Env Critique Edit Not reported Not reported
Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes

Mar 15, 2026

 Automatic Metrics Not reported Accuracy Not reported
Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning

Mar 9, 2026

 Automatic Metrics Not reported Accuracy, Cost Not reported
D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models

Feb 25, 2026

 Automatic Metrics Not reported Accuracy Not reported
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Feb 25, 2026

 Automatic Metrics Not reported Accuracy, Cost Not reported
Inducing Epistemological Humility in Large Language Models: A Targeted SFT Approach to Reducing Hallucination

Mar 18, 2026

 Not reported Pairwise Preference Not reported Not reported
Go-Browse: Training Web Agents with Structured Exploration

Jun 4, 2025

 Simulation Env Not reported Success rate Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (46.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (100% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (60% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (6.7% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (46.7% vs 35% target).

Strengths

  • Strong human-feedback signal (46.7% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 60% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.7% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (MMLU vs WebArena) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Judge vs Human AgreementBenchmark Slice: MMLUMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.7% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (7)
  • Simulation Env (5)
  • Llm As Judge (3)
  • Human Eval (1)

Human Feedback Mix

  • Pairwise Preference (3)
  • Demonstrations (2)
  • Critique Edit (1)
  • Expert Verification (1)

Top Benchmarks

  • MMLU (7)
  • WebArena (5)
  • GPQA (4)
  • MMLU Pro (3)

Top Metrics

  • Accuracy (7)
  • Cost (4)
  • Auroc (1)
  • Inference cost (1)

Top Papers On This Benchmark

Related Benchmark Hubs

MMLU Or WebArena Benchmark Papers MMLU Or AIME Or WebArena Benchmark Papers MMLU Or MATH-500 Or WebArena Benchmark Papers MMLU Or GPQA Benchmark Papers MMLU Or AlpacaEval Or WebArena Benchmark Papers MMLU Or SWE-bench Or WebArena Benchmark Papers MATH-500 Or GPQA Benchmark Papers (20) MMLU Or AIME Or AlpacaEval Benchmark Papers (48) MMLU Or AIME Or GPQA Benchmark Papers (42) GSM8K Or MMLU Or AIME Benchmark Papers (57) MMLU Or AIME Or MATH-500 Benchmark Papers (46) MMLU Or AIME Or SWE-bench Benchmark Papers (51) MMLU Or AIME Benchmark Papers (39) MMLU Or AlpacaEval Or GPQA Benchmark Papers (39) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (51) MMLU Or MATH-500 Or AlpacaEval Benchmark Papers (44) MMLU Or SWE-bench Or AlpacaEval Benchmark Papers (45) MMLU Or AlpacaEval Benchmark Papers (32) GSM8K Or MMLU Or GPQA Benchmark Papers (48) MMLU Or MATH-500 Or GPQA Benchmark Papers (39) MMLU Or SWE-bench Or GPQA Benchmark Papers (42) MMLU Or GPQA Benchmark Papers (29) GSM8K Or MMLU Or MATH-500 Benchmark Papers (50) GSM8K Or MMLU Or SWE-bench Benchmark Papers (55)
Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Create Account & Post a Job
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
Learn About Managed Service
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.
Join Now